Why Does ChatGPT Get Facts About My Business Wrong, and How Do I Fix It?

Have you ever asked ChatGPT about your own company?

Most business owners have. You type your company name into ChatGPT or Perplexity, and the answer comes back fast and confident. Sometimes it is right. Sometimes it invents a founding year, the wrong city, a service you dropped years ago, or a price you never charged. That gap matters more every month.

I work with founders who now hear the words 'I asked an AI about you' in sales calls. When the AI is wrong, the founder looks sloppy through no fault of their own. So let me walk through why this happens and what I actually do about it.

Why does ChatGPT get facts about my business wrong?

ChatGPT gets facts wrong because it predicts likely text, not verified truth. When it has thin or dated information about your company, it fills the gap with a confident guess. People call this a hallucination. The model is not lying on purpose. It simply has no strong source to anchor the answer.

Large models like GPT-5, Claude, and Gemini learn from huge amounts of web text. If your business is small, new, or lightly documented online, the model has little to go on. It then leans on patterns from similar companies, and those patterns are often wrong for you.

Where do AI tools get their information about my company?

They pull from public, structured, and widely repeated sources. Think of your own website, Wikipedia and Wikidata, your Google Business Profile, LinkedIn, Crunchbase, directories, news mentions, and review sites. Tools like Perplexity and Google AI Overviews also read live pages at answer time, so your current site matters a lot.

The key idea is repetition and agreement. When many trusted sources state the same clear fact about you, the model treats it as settled. When sources disagree or say nothing, you get invented details. That is why cleaning up your own footprint is the real work.

What kinds of errors show up most often?

The common ones are wrong location, wrong founding date, outdated services, mixed-up team names, and made-up pricing. I also see AI blend two similar companies into one. If another business shares part of your name, the model may merge your stories and hand the reader a confident mess.

These errors are not random. They cluster around facts that are missing from your site or stated in only one place. Every fact you leave vague is a fact the model may decide to guess.

The riskiest errors are the ones that sound plausible. A wrong founding year or a nearby city is easy to miss, because nothing about the sentence looks off. That is what makes AI mistakes different from a broken link. They are confident, well formed, and quietly false, so you only catch them if you go looking on purpose.

How do I find out what AI says about my business?

Ask the tools directly and write down what they say. I open ChatGPT, Gemini, Perplexity, and Google AI Overviews and ask simple questions. What does this company do, where are they based, who runs it, and what do they charge. I run each a few times, because answers shift between sessions.

I keep a short document of every wrong claim and the likely source behind it. If Perplexity cites a page, I click it. This gives me a punch list instead of a vague worry. You can also watch which AI tools send you visitors, which I cover in my guide on how to track ChatGPT and Perplexity traffic in Webflow Analyze.

How do I fix wrong facts on the sources AI reads?

Fix the facts at the source, then repeat them consistently. Start with your own website, since you control it fully. State your location, founding year, services, and team in plain text on your About and contact pages. Then align your Google Business Profile, LinkedIn, and any directory listings so they all agree.

Consistency is the lever. If your site says one city and LinkedIn says another, you are teaching the model to be unsure. I make the same core facts appear, word for word, across every profile I control. Boring repetition is what wins here.

I also make sure the important facts sit in plain text, not locked inside an image or a graphic. A model cannot read a founding date that only exists on a designed banner. If it matters, it belongs in real, selectable words that a crawler can actually pick up.

Does schema markup help AI understand my business?

Yes, structured data gives machines a clean version of your facts. Organization schema from schema.org lets you label your name, logo, location, and founder in a format that crawlers read easily. The sameAs property links your site to your LinkedIn, Crunchbase, and Wikidata entries, which helps tools connect the dots.

Schema is not magic, and it will not force an AI to quote you. It just removes ambiguity. I treat it as one part of a bigger trust picture, which I dig into in my piece on how AI entity recognition shapes business trust.

I keep my structured data simple and truthful. I do not stuff it with claims I cannot back up, because schema is meant to mirror what is already true on the page, not to inflate it. Clean, honest markup is the version that actually helps a model trust you.

Should I worry about AI Overviews and Perplexity too?

Yes, because they read live pages and cite them. Google AI Overviews and Perplexity often pull from the top pages for a query and summarize them on the spot. If your own site ranks well and states facts clearly, you have a real shot at being the source they quote instead of a random third party.

This is where classic SEO and newer answer optimization meet. Getting cited by these tools follows many of the same habits, which I lay out in my guide on how to get cited by ChatGPT, Perplexity, and Google AI Mode.

How often should I check what AI says about me?

Check once a quarter, and again after any big change. If you move offices, rename a service, add a partner, or shift your pricing, update every profile the same week. Then re-ask the AI tools a month later to see whether the new facts have started to show up.

Models refresh on their own schedule, so patience is part of the job. I have seen corrections take weeks to appear. The point is to keep the true version everywhere, so that when the model does refresh, it has only good material to learn from.

What is the realistic goal here?

The goal is not perfect control, it is fewer confident mistakes. You cannot edit a model's memory by hand. You can make the truth about your business clear, consistent, and easy to find, which pushes the odds in your favor over time.

If an AI is telling people the wrong story about your company, I am happy to help you audit what it says and tidy the sources behind it. Let's chat, and we can build you a cleaner, steadier footprint that both people and machines can trust.